Abstract: System and methods for tuning equation of state (EOS) characterizations are presented. Pressure-volume-temperature (PVT) data is obtained for downhole fluids within a reservoir formation. A component grouping for an EOS model of the downhole fluids is determined, based on the obtained PVT data. The component grouping is used to estimate properties of the downhole fluids for a current stage of a downhole operation within the formation. A machine learning model is trained to minimize an error between the estimated properties and actual fluid properties measured during the current stage of the operation, where the component grouping for the EOS model is iteratively adjusted by the machine learning model until the error is minimized. The EOS model is tuned using the adjusted component grouping. Fluid properties are estimated for one or more subsequent stages of the downhole operation to be performed along the wellbore, based on the tuned EOS model.

Abstract: Computer systems and associated methods are disclosed to implement a model development environment (MDE) that allows a team of users to perform iterative model experiments to develop machine learning (ML) media models. In embodiments, the MDE implements a media data management interface that allows users to annotate and manage training data for models. In embodiments, the MDE implements a model experimentation interface that allows users to configure and run model experiments, which include a training run and a test run of a model. In embodiments, the MDE implements a model diagnosis interface that displays the model's performance metrics and allows users to visually inspect media samples that were used during the model experiment to determine corrective actions to improve model performance for later iterations of experiments. In embodiments, the MDE allows different types of users to collaborate on a series of model experiments to build an optimal media model.